Abc on Plant Performance

Available online at www. science prepargon. com Accounting, Organizations and companionship 33 (2008) 119 www. elsevier. com/locate/aos The employment of manufacturing practices in mediating the touch of employment-based be on countersink professionalcedure Rajiv D. Banker a, Indranil R. Bardhan b b,* , Tai-Yuan subgenus Chen c a Fox civilise of Business, Temple University, 1810 N. 13th Street, Philadelphia, PA 19122, USA The University of Texas at D altogetheras, School of Management, SM 41, 2601 N.Floyd Road, Richardson, TX 75083-0688, USA c School of Business and Management, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong, China Abstract We try the jolt of activity-based be (first principle) on betrothal of initiative manufacturing (WCM) practices and whole kit act. In contrast to forward enquiry that supposes the direct regard of first principle on coif military operation, we gird an alternating(a) look determine to r uminate the piece of world-class manufacturing practices as a intermediator of the preserve of rudiment.Analysis of entropy from a tremendous cross-sectional try on of US manufacturing establishs evokes that alphabet has no signi? after partt direct tint on nominate cognitive operation, as measured by advancements in whole manufacturing calls, cycle epoch, and proceeds lumber. We ? nd, however, that WCM practices offly mediate the positive sham of rudiment on lay down mathematical exhibit, and thus ripe(p) manufacturing capabilities counterbalance a captious missing link in infering the everyplaceall relate of first principle. Our results brook a di? rent abstract lense to evaluate the birth amongst first principle bankers acceptance and instal work on, and aro physical exertion that first rudiment drag inion by itself does non amend place applyation. O 2006 Elsevier Ltd. All rights reserved. Introduction Activity-based be ( first r udiment) was designed with the objective of providing omnibuss with consummate activity-based cost information by employ cost drivers to assign activity cost to harvest-feasts * Corresponding author. Tel. +1 972 883 2736 facsimile +1 972 883 6811. E-mail addresses emailprotected edu (R. D. Banker), emailprotected edu (I. R. Bardhan), emailprotected k (T. -Y. Chen). and services. Prop hotshotnts of first rudiment argue that it cans accu enumerate cost information ask to make appropriate strategic decisions in terms of fruit liquify, sourcing, pricing, address improvement, and evaluation of blood demonstrate transaction ( make & Kaplan, 1992 Swenson, 1995). These claims whitethorn bind got led m both(prenominal) ? rms to adopt first principle organizations. A muckle of the coke0 prodigiousst ? rms in the unite Kingdom showed that 19. 5% of these companies establish pick out first principle (Innes & Mitchell, 1995). An an classify(prenominal) stick w ith rel chastenessd by the Cost Management 0361-3682/$ hold in front matter O 2006 Elsevier Ltd.All rights reserved. doi10. 1016/j. aos. 2006. 12. 001 2 R. D. Banker et al. / Accounting, Organizations and conjunction 33 (2008) 119 Group (1998) of the Institute of Management Accountants indicated that 39% of organizations do approved first principle borrowing. 1 Assessing the wedge of first principle on manufacturing prove carrying out is recognized as an crucial look for question. Prior question has typically focused on the direct impact of first rudiment eyepatch ignoring its indirect impact in accommodateing otherwisewise organizational capabilities. piece of music past studies produce depend moderate levels of bene? s from alphabet adoption (Foster & Swenson, 1997 Ittner & Larcker, 2001), few have extended this work to evaluate the linkages amid beliefs that re show advantageful outcomes and the operational measures of limit achievement. Further much , the de? nition of rudiment success has often been vaguely de? ned in terms of subjective beliefs regarding ? nancial bene? t, satisfaction with first principle, or use of rudiment system for decision making. In decrease of these methodological de? ciencies, we argue that a to a greater extent than rigorous set about is emergencyed to measure the impact of first principle.It is withal important to focus on process-level surgical procedure measures, instead of ? rm-level ? nancial metrics, since the potential impact of alphabet murder may be appropriated before they ar re? ected in a ? rms conglomeration writ of execution. Evidence of past rudiment fulfilation failures have led questioners to suggest that rudiment success depends on other contextual and process federal agents, such(prenominal)(prenominal) as organizational structure, task characteristics, c ar take, information engineering science, and the external environs (Anderson, Hesford, & preteen, 2002).In this accept, we focus on the mechanism by means of which first principle impacts workings military operation, in terms of its role as an alterr of organizational capabilities rather than its direct impact. Speci? cally, we study the association amongst instruction execution of alphabet and world-class manufacturing (WCM) capabilities, and their impact on set outlevel operational military operation. Using a large cross-sectional stress of US manufacturing coiffures, we ? nd that ABC has a positive association with the learning of process-centric capabili carrying out of ABC has been observed not scarcely when in manufacturing ? rms but similarly in service arna ? rms (Cooper & Kaplan, 1992). ties required to successfully implement WCM. We as well ? nd that ABC does not have a signi? wobble direct impact on specify performance measures. Instead, its impact on ingraft performance is intermediate done the beginment of WCM capabilities, which permit positions to leverage the process capabilities o? ered by ABC into signi? bank improvements in nominate performance. Our study makes contributions in some(prenominal) aras. Our central contribution involves the development of an empirically validated influence which indicates that the impact of ABC on industrial give performance is completely mediated through its enablement of WCM capabilities.Second, since ABC is implemented and used at the business organisation process level, we focus our attention on operational process performance measures by parcel outing the manufacturing demonstrate as a unit of analysis. This allows us to avoid the drawbacks associated with foregoing studies which have mostly focused on aggregated, ? rm-level ? nancial measures. Third, our results suggest that the conceptual lens through which antecedent research has traditionally studied the impact of ABC call for to be revisited and validated employ di? erent types of siting and measuremen t climb upes. adverse to the ? dings of Ittner, Lanen, and Larcker (2002) we ? nd that, although the direct impact of ABC is not signi? hawk, ABC has a statistically signi? move indirect e? ect on sow performance that is mediated through its support for advanced manufacturing capabilities. The rest of our make-up is organized as follows. In the next section, we review the associate books on ABC, advanced manufacturing practices, and whole kit and boodle performance. We then(prenominal) present our conceptual research framework and research hypotheses, followed by a description of our research selective information and design.Next, we describe our statistical estimation results, followed by a discussion of our results, contributions, and limitations. We summ trick up our ? ndings and the implications of our study in the last section. Background The ABC lit de? nes an activity as a discrete task that a ? rm undertakes to make or deliver R. D. Banker et al. / Accounting, Orga nizations and conjunction 33 (2008) 119 3 a intersection point/service, and uses cost drivers to assign activity costs to products, services or customers related to these activities (Cooper, 1988 Ittner et al. 2002). Traditional costing systems use bases like direct dig up and machine hours to allocate expenses, associated with indirect and support activities, to products and services. On the other hand, ABC segregates the expenses of indirect and support resourcefulnesss by activities, and then assigns those expenses based on the drivers of these activities (Cooper & Kaplan, 1991). Hence, ABC provides prove mangers with a more(prenominal) structured approach to evaluate the expenses associated with speci? c activities used to support a product.The body of earlier research regarding the impact of ABC has produced mixed evidence. On one hand, proponents of ABC have argued that ABC helps to capture the economics of output signal processes more closely than traditional cost-bas ed systems, and may provide more accurate costing data (Cooper & Kaplan, 1991 Ittner, 1999). Prior research suggests that effectuation of ABC should lead to operational and strategic bene? ts within organizations (Anderson & Young, 1999 Cooper & Kaplan, 1991). investigateers have argued that operational bene? s may emanate from improved visibleness into the (a) economics of the end product processes, and (b) causal cost drivers. Strategic bene? ts may arise from avail super source of ruin information for product development, sourcing, product mix and other strategic decisions (Anderson, 1995 Shields, 1995). searchers have claimed that, since ABC may provide greater visibleness into business processes and their cost drivers, it may allow managers to eliminate costs related to non- valuate added activities and improve the e? ciencies of existing processes (Carol? , 1996).Improved information visibility may also enable the deployment of quality-related initiatives by identifying activities that are associated with poor product quality, and their cost drivers (Ittner, 1999 Cooper, Kaplan, Maisel, Morrissey, & Oehm, 1992). Hence, preliminary research suggests that ABC may be associated with adoption of process improvement activities, such as total quality management (TQM) programs (Ittner & Larcker, 1997a, 1997b Anderson et al. , 2002). On the other hand, selective informationr and Gupta (1994) claimed that increasing the be of cost pools and improving the speci? ation of cost bases may sum up the frequency of errors in product cost measurement. Banker and Potter (1993) and Christensen and Demski (1997) suggest that the ability of ABC to produce accurate cost estimates depends on other factors, such as the competitiveness of grocerys and the quality of the organizations information technology infrastructure. Noreen (1991) suggests that ABC execution may provide bene? cial results only under speci? c conditions. Similarly, empirical studies that have w atchd the impact of ABC on ? m performance have also produced mixed results (Ittner & Larcker, 2001 Gordon & Silvester, 1999). Many of these studies rely on managers beliefs regarding the success of ABC writ of execution, but they do not indicate whether ABC adopters achieved high uper levels of operational or ? nancial performance compared to non-adopters (Shields, 1995 McGowan & Klammer, 1997 Foster & Swenson, 1997). former(a) studies have suggested that many ABC adopters have abandoned their instruction executions, raising concerns to the highest degree the potential impact of ABC on performance (McGowan & Klammer, 1997). In this study, e search the relationships between ABC implementation and WCM practices, and their impact on launch performance. Unlike prior studies, which focus on touchstone the direct impact of ABC on instal performance, our focus is directed at the role of ABC as an enabler of WCM practices which, in turn, have an impact on whole works performance. In their study on relationships between incentive systems and JIT implementation, Fullerton and McWatters (2002, p. 711) note that the pitch to world-class manufacturing strategies requires accompanying changes in ? rms management accounting systems.They argue that by providing a rectify registering of the inter-relationships between manufacturing processes, demand uncertainty and product complexity, ABC implementation allows plant managers to direct germane(predicate) process improvements which still implementation of other WCM initiatives. Cooper and Kaplan (1991) also claim that ABC may help plant managers to develop a develop 4 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 understanding of the sources of cost variability, which allows them to manage resource demand and rationalize changes in product mix.The arguments in support of ABC are based on the presumed comparative advantage that ? rms may get ahead from greater transparency and accurac y of information obtained from ABC (Cagowin & Bouwman, 2002). However, Kaplan (1993) and others have cautioned that not every ABC implementation will produce direct bene? ts. Indeed, the role of other facilitators and contextual factors, such as implementation of related organizational initiatives, has gained greater importance in this debate (Anderson et al. , 2002 Henri, 2006).A fundamental motivation of our research is to better understand the everyplaceall impact of ABC on plant performance by studying its indirect impact on plant WCM capabilities. We argue that ABC implementation should impact plant performance only by supporting the implementation of advanced manufacturing capabilities, which provide managers with the ? exibility to adapt to changing product and demand characteristics. Without such capabilities, ABC is unpotential to improve manufacturing performance by itself. Unlike anterior studies that have studied the impact of ABC on ? rm-level performance, we bserve that isolating the impact of ABC at the plant-level allows us to vestige ABCs impact on speci? c plant performance measures, and overcomes the potential for confounding when multiple business processes are aggregated at the ? rm level. We discuss our conceptual framework and research hypotheses in the next section. conceptual research warning We posit that adoption of ABC by itself may not provide much direct value, but may quicken the implementation of advanced manufacturing practices and other organizational capabilities which, in turn, may be associated with sustainable improvements in plant performance.Unlike previous research that has in the large part explored the direct impact of ABC, our research poseur allows for the possibility of plant performance improvements due to implementation of WCM practices that may be enabled by capabilities associated with the adoption of ABC systems. WCM practices entail a broad lean of manufacturing capabilities, which allow plant manager s to adapt to the volatility and uncertainty associated with changes in customer demand and business cycles in agile manufacturing environments (Flynn, Schroeder, & Flynn, 1999 Sakakibara, Flynn, Schroeder, & Morris, 1997 Banker, Potter, & Schroeder, 1995).These practices include just-in- metre manufacturing (JIT), unremitting process improvement, total quality management (TQM), competitive benchmarking, and worker autonomy through the use of autonomous work teams. Advanced manufacturing practices provide the capabilities necessary to react to rapid changes in lot sizes and setup times, as the manufacturing focus shifts to ? exible and agile processes that are characterized by quick changeover techniques to handle production of low volume orders with high product variety (Kaplan, 1983 Flynn et al. 1999). Traditional costing systems, which are based on assumptions of long production runs of a standard product with static speci? cations, are not relevant in such dynamically changing environments. However, proponents have argued that ABC may provide more accurate information on the activities and transactions that impact product costs in manufacturing environments characterized by production of smaller lot sizes, high broad mix, and frequent changeovers (Krumwiede, 1998). By providing seasonably information about the costs of esources, especially when production runs are shorter or the production method changes, ABC implementation may provide the process infrastructure necessary to support managerial decision-making capabilities in fast-paced manufacturing processes (Kaplan, 1983). Hence, we study the impact of ABC on its ability to support implementation of WCM capabilities, and examine its indirect impact on plant performance through its enablement of such capabilities. Our conceptual research present describing the relationship between ABC, manufacturing capabilities and plant performance is shown in Fig. . The puzzle comprises of ii stages. The ? rst st age describes how ABC may comfort implementation of world-class manufacturing practices. R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 5 Activity-based Costing (ABC) H1 ?QUALITY H2 ? TIME H3 ? COST World-class Manufacturing (WCM Plant surgery sizing PLANTAGE trenchant DOWNSIZE tidy sum jumble Plant-level Control inconstants Plan Fig. 1. conceptual research fabric. Note Plant performance is represented exploitation ternary separate dependent versatiles that are grouped together in the gure for ease of representation. Our regression models are estimated utilise from each one performance covariant as a dependent variable in a separate variable regression. The second stage describes the impact of advanced manufacturing capabilities, as embodied by WCM, on plant performance. The key di? erence between our research model and that of prior studies is our focus on the relationship between ABC and WCM, and the role of manufacturing capabilities as a m ediator of the impact of ABC on plant performance, as represented by the dotted arrow in Fig. 1. carry on of activity-based costing on world-class manufacturing In his early work on the challenges of implementing new types of management accounting models to measure manufacturing performance, Kaplan (1983, p. 702) noted that . . . accounting systems must be tightly integrated with plant production planning and scheduling systems so that production managers are rewarded for e? cient utilization of bottleneck resources and reduced inventory levels passim the plant. . . . Prior research has suggested that ABC is more bene? cial when it supports the implementation of advanced manufacturing practices (Shields & Young, 1989Kaplan, 1992 Cooper, 1994). For example, Anderson and Young (1999) reviewed several ABC studies that worked positive relations between the success of ABC adoption and implementation of various advanced manufacturing practices. They argue that ABC facilitates more accur ate identi? cation and measurement of the cost drivers associated with value added and non-value added manufacturing activities, which makes it easier to develop better cost tick off and resource allocation capabilities necessary prerequisites for successful implementation of worldclass manufacturing.In world-class manufacturing environments, the accounting systems, compensation, incentive structure, and performance measurement practices are di? erent from those that are used in traditional manufacturing (Miltenburg, 1995 Milgrom & Roberts, 1995). For example, traditional manufacturing processes entail the use of performance measures that cut unit manufacturing costs related to (a) equipment utilization, (b) ratios of direct and indirect fag out to volume, (c) number of set-ups, and (d) number of orders. On the other hand, erformance measures relevant to WCM implementation track (a) actual cost and quality, (b) cycle time reduction, (c) deliverance time and ontime delivery rate , and (d) actual production as a percentage of planned production (Miltenburg, 1995, p. 336). By enable the measurement of costs related to speci? c activities, products, and customers, ABC may provide more accurate identi? cation and measurement of new types of performance measures that are a critical component of successful WCM implementations (Argyris & Kaplan, 1994 Krumwiede, 1998).Proponents claim that ABC may support the implementation of WCM capabilities in several ways. First, by allowing plant managers to track costs accurately and enabling identi? cation of redundant resources, ABC may support implementation of TQM and other quality/process improvement programs. 2 Second, ABC may support process-related investments in cycle time study Ittner (1999) for an example of the bene? ts of activitybased costing for quality improvement at a telecommunications ? rm. 2 6 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 reduction by facilitating the timely id enti? ation of non-value-added activities (Kaplan, 1992). Third, ABC may allow plant managers to make better resource allocation decisions by focusing the product line and accurately anticipating the e? ect of changes in the product mix on the pro? tability of manufacturing operations. Hence, they argue that ABC implementation may provide the process correspond necessary to analyze activities, pucker and trace costs to activities, and establish relevant output measurescapabilities that are useful in ? exible manufacturing environments (Cooper & Kaplan, 1991, 1999).Implementation of ABC may be associated with greater use of self-directed teams and worker autonomy, which are also important capabilities of WCM (Anderson & Young, 1999). Similarly, best practices data on cost pools, activity centers, and cost drivers can be incorporated into the design and use of ABC systems which may improve plant managers abilities to make better strategic product decisions, and thereby support imple mentation of WCM programs (Elnathan, Lin, & Young, 1996 Atkinson, Banker, Kaplan, & Young, 2001). Therefore, we posit that ABC facilitates successful implementation of WCM capabilities.In contrast to Ittner et al. (2002), who treat advanced manufacturing practices as causal variables in exempting adoption of ABC, we posit that ABC supports implementation of WCM practices, which in turn, may improve plant performance. Accordingly, Hypothesis H1 Plants which implement ABC are more credibly to implement world-class manufacturing practices. Impact of world-class manufacturing on plant performance Implementation of WCM practices can enable plants to react quickly to changes in customer demand, and thereby carry raze levels of inventory, improve cost e? iencies, increase the ? exibility of production facilities through use of planning and scheduling software, and improve boilersuit plant productiveness (Banker, Bardhan, Chang, & Lin, 2006). Investments in JIT and ? exible manufacturing practices help to reduce setup times that permit shorter production runs, thereby allowing for more e? cient inventory control, as well as disgrace product defect rates (Kaplan, 1983 Hendricks & Singhal, 1997 Sakakibara et al. , 1997).Techniques that are commonly deployed, within the stove of JIT implementations, include pull/Kanban systems, lot-size reductions, cycletime reductions, quick changeover techniques, and bottleneck removal practices. Research on the performance impact of JIT has been extensively documented in the books (Sakakibara et al. , 1997 Hendricks & Singhal, 1997). Reported bene? ts range from reduced work in progress and ? nished goods, to better quality and higher ? rm productivity. Based on prior empirical evidence, researchers have found that ? ms which adopted JIT production are better reorient to customer needs, have shorter lead times, and faster time to securities industry (Srinivasan, Kekre, & Mukhopadhyay, 1994). Implementation of WCM practices als o entails adoption of other process improvement practices, such as total quality management (TQM) and continuous process improvement programs (Fullerton & McWatters, 2002). The fundamental elements of process improvement programs consist of competitive benchmarking, statistical process control, and employee mandate (Schroeder & Flynn, 2001).Such process improvement practices, stemming from greater attention to product quality and time to market issues may enable manufacturing plants to develop advanced manufacturing capabilities. Based on ? rm-level data, researchers have found that implementation of TQM and other advanced manufacturing practices have a positive impact on ? rm performance, through realization of lower product cost, higher quality, and better on-time delivery performance (Banker, Field, & Sinha, 2001 Banker et al. , 1995 Hendricks & Singhal, 1997 Ittner & Larcker, 1995, 1997a).Hence, we posit that implementation of WCM practices in manufacturing plants may be positi vely related to improvements in plant-level performance as de? ned by plant cost, quality and time-to-market measures. Therefore, we hypothesize that R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 7 Hypothesis H2 Plants that have implemented WCM practices are more potential to be associated with signi? cant improvements in plant performance. H2a Plants which implement WCM practices are more likely to realize improvements in plant manufacturing costs.H2b Plants with WCM practices are more likely to realize improvements in plant quality. H2c Plants with WCM practices are more likely to realize improvements in time to market. Impact of ABC on plant performance a mediation mechanism Proponents have argued that, by enabling easier identi? cation of non-value added activities and simpli? cation of cost measurements, ABC enables implementation of advanced manufacturing practices, especially in processes that are characterized by quick changeovers and a range of s upport activities. Documenting and understanding activities is a necessary prerequisite to improving business processes, since activities are the building blocks of business processes. If ABC adoption results in more accurate costing then plant performance may improve because of greater ability to implement process improvement initiatives, facilitating the simpli? cation of business processes by removing non-value added activities. Successful implementation of WCM practices requires the development of business process models to identify and eliminate non-value added activities.In this respect, ABC implementation entails a priori development of such process models to identify and analyze activities, trace costs to activities, and analyze activity-based costs. Similarly, plant managers can use information gathered through ABC analyses to conduct a Pareto analyses of the major cost drivers, an important agent in most TQM and competitive bench3 marking initiatives. Scenario analysis re lated to pricing, product mix, and pro? tability is also possible, which are useful in the deployment of JIT capabilities.Hence, successful WCM implementations may leverage the streamlining of business processes due to ABC adoption. ABC analyses allow plants to develop activitybased management (ABM) business models which managers may adopt to improve their organizational e? ectiveness (Chenhall & Lang? eld-Smith, 1998). In addition, ABC implementation may be correlated with and and then serve as a surrogate for unobservable factors, such as management leadership and worker training, that are important components of successful WCM implementation. Hence, implementation of WCM may allow plants to leverage the capabilities o? ered by ABC (i. . accurate cost allocations and management support) into improvements in plant performance. Our approach di? ers from the prior literary works which has primarily studied the direct impact of ABC on plant performance (Ittner et al. , 2002). Inste ad, we argue that it is important to view the role of ABC as a potential enabler of manufacturing capabilities, and study its indirect impact on plant performance as completely mediated by WCM. This perspective argues that ABC may support improvements in manufacturing capabilities which are, in turn, associated with improvements in plant performance (Henri, 2006).Hypothesis H3 The positive association between ABC implementation and plant performance is mediated through implementation of worldclass manufacturing practices. An alternative perspective, with respect to the role of ABC, is that the fundamental fundamental interaction between WCM capabilities and ABC implementation may jointly determine plant performance. The interaction perspective argues that advanced manufacturing capabilities, when combined with deployment of ABC methods, create complementarities that rationalize variations in plant performance (Cagowin & Bouwman, 2002). In other words, WCM and ABC may each have a d irect e? ct on performance, but would add more value when used in crew (i. e. , the presence of WCM will increase the Low volume production creates more transactions per unit manufactured than high volume production (Cooper & Kaplan, 1988). 8 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 strength of the relationship between ABC and performance). In this framework, the interaction e? ects of ABC and WCM need to be estimated to study the overall impact of ABC on plant performance. We explore the interaction perspective further when we discuss our estimation results. Fig. represents the conceptual research model that describes our hypothesized relationship between ABC and implementation of WCM practices, and the role of WCM as a mediator of the impact of ABC on plant performance. Research design We now describe the characteristics of the data collected and approach for measuring the variables of interest in our study. Data collection Data for this research was bony from a survey of manufacturing plants across the US, conducted in the year 1999 by IndustryWeek and PricewaterhouseCoopers Consulting. The survey consisted of a questionnaire which was mailed to plants with two-digit standard industrial classi? ation (SIC) codes from 20 to 39, and that employed a minimum of cytosine people. Data were collected on a range of manufacturing, management and accounting practices used within each plant. We have draw the questions relevant to our research model in Appendix. The survey was mailed to more or less 27,000 plant managers and controllers from IndustryWeeks database of manufacturing plants. Plant managers provided data on the achievement of implementation of ABC and a broad range of advanced manufacturing practices and plant characteristics. Data on plant performance measures were based on assessments of plant records by plant controllers. A total of 1757 plants responded to the questionnaire for an overall result rate of 6. 5%. Th e usable sample contains 1250 plants that provided Since data on the independent and dependent variables was provided by di? erent sources, this mitigates the concerns associated with common methods bias. 4 complete responses to the variables of interest in our model. 5 We present the distribution of the manufacturing plants in our sample by industry in confuse 1, and compare it to the distribution of manufacturers, reported in the Statistical Abstract of the United States and published by the US count Bureau (2000).Since we obtained the data from a secondary data source, we did not have information with respect to the pro? les of non-respondent plants. To evaluate the generalizibility of our ? ndings, we compared the average out plant productivity per employee of our sample plants to the average productivity of all US manufacturing plants, as reported by the US Census Bureau (2000). The average plant productivity per employee of our sample was $221,698, while the average product ivity in the US Census data was reported to be $225,440. The di? erence in average plant productivity was not statistically signi? cant (t-statistic = 0. 37 p-value = 0. 35).Measurement of variables The ABC adoption variable was de? ned based on the response to the survey question asking whether ABC was implemented at the plant (0 = not implemented, 1 = plan to implement, 2 = extensively implemented). For the purpose of our study, we collapsed the ? rst two categories into one category, which represents plants that have not implemented ABC at the time of the survey. Hence, we measure ABC as a 01 knocker variable where zero represents no implementation and one represents extensive implementation. The number of plants that have adopted ABC extensively in our sample is 248, an adoption rate of 19. 8%.We have three dependent variables in our research model. The variable DCOST denotes the change in unit manufacturing costs in the last ? ve eld. DQUALITY denotes the change in plant ? rs t-pass quality yield in the last ? ve long time. DTIME 5 While the net usable response rate of 4. 6% is small, it is comparable to large plant operations surveys as reported in Stock, Greis, and Kasarda (2000) and Roth and van der Velde (1991). R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 shelve 1 Distribution of sample plants by industry Industry sector Non-durable manufacturing Food and kindred products Tobacco products Textile ill products Apparel and other textile products Lumber and wood products Furniture and ? xtures Paper and allied products stamp and publishing Chemicals and allied products Petroleum and coal products Durable manufacturing Rubber and plastics products flog and leather products Stone, clay and glass products Primary alloy industries Fabricated metal products Industrial machinery and equipment Electronics and electrical equipment Transportation equipment Instruments and related products Miscellaneous manufacturing Total a b 9 SIC code Number of plants in sample 47 1 23 13 25 43 56 19 86 5 74 5 39 67 153 225 168 103 76 22 1250Percent of sample 3. 76% 0. 08 1. 84 1. 04 2. 00 3. 44 4. 48 1. 52 6. 88 0. 40 5. 92 0. 40 3. 12 5. 36 12. 24 18. 00 13. 44 8. 24 6. 08 1. 76 100% Percent of US manufacturersa 5. 76% 0. 03 1. 70 6. 45 10. 13 3. 33 1. 79 17. 19 3. 41 0. 59 0. 52 0. 51 4. 52 1. 73 10. 47 15. 54 4. 71 3. 41 3. 23 4. 97 100% % ABC Adopters in sampleb 12. 76% 100 21. 74 38. 46 16. 00 27. 91 28. 57 26. 32 26. 74 40. 00 13. 51 40. 00 20. 51 16. 42 16. 99 13. 03 19. 05 26. 21 17. 11 31. 82 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Source US Census Bureau (2000).The percentage gibes the number of ABC adopters divided by the number of plants in the 2-digit SIC group. represents a factor comprising of the change in manufacturing cycle time and the change in lead time during the last ? ve years, and thus is indicative of the time to market for each plant. The measurement scale of the plant per formance variables was ordered in manner such that higher set represent improvements in performance over time. 6 WCM represents a composite factor that consists of 6-spot types of advanced manufacturing practices, as described in the survey questionnaire.The six indicators were measured using a 01 scale, where zero represents no or well-nigh implementation, and one indicates extensive implementation. Next, we constructed WCM as a six-item 6 A value of DQUALITY = 1 indicates that ? rst-pass quality yield declined more than 20%, while DQUALITY = 5 indicates that quality yield improved more than 20%. On the other hand, DCOST = 1 indicates that unit manufacturing costs increase more than 20%, while DCOST = 7 suggests that costs reduced more than 20%. summative exponent that represents the degree of implementation of the six types of advanced manufacturing capabilities. This index measures both the range and depth of manufacturing capabilities in each plant. Hence, for each plant, WC M consists of seven-spot levels and can take any value between zero and six (since the six indicators are measured as 01 variables). Our approach for constructing this summative measure of manufacturing capability is consistent with similar approaches in the literature (Krumwiede, 1998 Loh & Venkatraman, 1995) that use a summative index when an increase in any of the indicators is associated with a corresponding increase in the construct of interest.We note that alpha factor analyses (EFA) suggests that the six items load on a single(a) factor (with Eigen value = 2. 13) which accounts for 36% of magnetic variation in the data. Furthermore, the EFA provides support for the validity and unidimensionality of the WCM factor. 7 10 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 (0. 07) (0. 00) (0. 01) (0. 27) (0. 01) (0. 41) (0. 87) (0. 02) (0. 00) (0. 00) (0. 72) (0. 00) (0. 76) (0. 79) (0. 68) (0. 05) (0. 40) (0. 60) (0. 00) (0. 04) (0. 00) (0. 96) (0. 04) (0. 29) (0. 00) (0. 00) (0. 60) 0. 06 0. 21 A0. 00 0. 06 A0. 03 A0. 13 0. 8 A0. 01 1. 00 0. 18 0. 29 1. 00 7. 00 4. 53 5. 00 1. 46 (0. 45) (0. 20) (0. 00) (0. 22) (0. 34) (0. 00) ABC WCM discrete DOWNSIZE SIZE PLANTAGE garishness MIX DCOST DQUALITY DTIME Minimum Maximum Mean normal Std. Dev. 1. 00 0. 12 A0. 03 0. 02 0. 05 0. 01 0. 02 0. 01 0. 06 0. 01 0. 06 0. 00 1. 00 0. 19 0. 00 0. 39 (0. 00) (0. 22) (0. 40) (0. 06) (0. 86) (0. 46) (0. 81) (0. 03) (0. 59) (0. 04) 0. 11 1. 00 A0. 01 0. 03 0. 22 A0. 03 0. 09 0. 03 0. 23 0. 25 0. 31 0. 00 6. 00 4. 00 4. 00 1. 61 (0. 70) (0. 35) (0. 00) (0. 24) (0. 00) (0. 22) (0. 00) (0. 00) (0. 00) A0. 03 A0. 03 1. 00 A0. 09 0. 03 A0. 06 A0. 8 0. 04 A0. 00 0. 01 0. 08 0. 00 1. 00 0. 59 1. 00 0. 49 (0. 00) (0. 33) (0. 02) (0. 00) (0. 15) (0. 90) (0. 74) (0. 00) 0. 02 0. 04 A0. 08 1. 00 0. 03 0. 10 A0. 02 0. 01 0. 06 0. 01 A0. 03 1. 00 3. 00 1. 75 2. 00 0. 76 (0. 29) (0. 00) (0. 38) (0. 60) (0. 04) (0. 64) (0. 28) 0. 05 0. 21 0. 03 0. 03 1. 00 0. 06 0. 20 0. 04 A0. 02 0. 03 0. 07 1. 00 5. 00 2. 73 2. 00 1. 08 (0. 04) (0. 00) (0. 17) (0. 53) (0. 35) (0. 01) (0. 09) (0. 00) (0. 30) (0. 22) 0. 02 A0. 01 A0. 07 0. 10 0. 08 1. 00 A0. 07 0. 06 A0. 12 A0. 04 A0. 29 1. 00 4. 00 3. 57 4. 00 0. 78 (0. 01) (0. 02) (0. 00) (0. 12) (0. 30) (0. 47) (0. 9) (0. 01) (0. 00) (0. 00) 0. 02 0. 08 A0. 18 A0. 02 0. 19 A0. 07 1. 00 A0. 22 0. 08 0. 02 A0. 02 0. 00 1. 00 0. 54 1. 00 0. 50 (0. 46) (0. 01) (0. 00) (0. 42) (0. 00) (0. 01) (0. 00) (0. 00) (0. 52) (0. 54) 0. 01 0. 04 0. 04 0. 01 0. 04 0. 09 A0. 22 1. 00 A0. 02 A0. 01 0. 07 0. 00 1. 00 0. 75 1. 00 0. 43 (0. 81) (0. 18) (0. 15) (0. 66) (0. 15) (0. 00) (0. 00) (0. 510) (0. 78) (0. 02) (0. 00) (0. 00) 0. 01 0. 24 0. 01 0. 01 0. 01 A0. 05 0. 02 A0. 01 0. 18 1. 00 0. 26 1. 00 6. 00 3. 14 3. 00 0. 90 p-Values are shown in parentheses. Spearman correlation coe? cients are in the height triangle and Pearson coe? ients are in the bottom triangle. (0. 00) 0. 05 0. 31 0. 08 A0. 03 0. 08 A0. 02 A0. 00 0. 06 0. 29 0. 26 1. 00 1. 00 6. 00 3. 30 3. 50 0. 86 plank 2 Descriptive statistics and correlations of model variables (N = 1250) Estimation results First, we estimate the impact of ABC on the implementation of WCM using an ordered logit regression model, where the dependent variable represents an ordered choice variable of seven possible states of WCM implementation WCM = 0 (no or some implementation on all six indicators) and WCM = 6 (extensive implementation on all six indicators).Our methodology is consistent with Krumwiedes (1998) approach to evaluate the antecedents of di? erent stages of ABC implementation in ABC WCM DISCRETE We include additional variables to control for the impact of plant characteristics on manufacturing capabilities and plant performance. There are six control variables in our model, which include plant size (SIZE) measured in terms of number of employees, plant age in years (PLANTAGE), record of manufacturing operations (DISCRETE), degree of produ ct mix (MIX), product volume (VOLUME), and the extent of downsizing in the last ? ve years (DOWNSIZE).Larger plants are more likely to have the scale and ? nancial resources required to justify adoption of advanced manufacturing practices and activity-based costing programs. SIZE is likely to impact plant performance since smaller plants are likely to be more agile in responding to customer needs compared to larger plants ceteris paribus (Hendricks & Singhal, 1997). Plant AGE is also likely to play a signi? cant role since older plants are less likely to adopt advanced manufacturing practices and often fail to realize the impact of technology-enabled processes on plant performance. Product MIX is de? ed as the mix of products produced and is measured as a binary variable based on low or high product diversity. Plants with high product diversity are more likely to implement ABC (Cooper, 1989) as it may provide more accurate estimates of overhead usage. DISCRETE represents a binary va riable with a value of one if the nature of manufacturing for primary products is discrete manufacturing, and zero for process or hybrid manufacturing. Descriptive statistics of our model variables are provided in postpone 2. DOWNSIZE SIZE PLANTAGE VOLUME MIX DCOST DQUALITY DTIME R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 1 manufacturing ? rms. Tests for multicollinearity (Belsley, Kuh, & Welsch, 1980) indicated no evidence of multicollinearity in our data (BKW index = 1. 06, variance in? ation factor = 1. 15). Our ordered logit regression results are presented in submit 3. The logit coe? cient column reports the results of an ordered logit test for the seven states of WCM. The logit results indicate that our model has real informative power (Chi-square = 82. 67 pseudo R2 = 0. 07). The ordered logit coe? cients indicate that adoption of ABC has a positive impact on WCM implementation (coe? ient value = 0. 499 v2 = 15. 15 p-value 0. 0001). Hence, o ur results support hypothesis H1, and suggest that plants that implement ABC are more likely to implement WCM practices. The ordered logit results also indicate that plant SIZE and product VOLUME have a positive impact on the extent of WCM implementation. Larger plants may be more likely to implement WCM capabilities due to availability of greater plant resources, and plants with high VOLUME may be more likely to implement WCM to deal with the complexity involved in managing high volume production.The mediating role of WCM Next, we estimate the impact of ABC and WCM on the three measures of plant performance, DCOST, DQUALITY, and DTIME, using ordinary to the lowest degree squares (OLS) regressions. For each dependent variable, we estimate the relationships between ABC, WCM and plant performance as speci? ed by the side by side(p) system of equations DPERFORMANCE ? a0 ? a1 A ABC ? a2 A DOWNSIZE ? a3 A SIZE ? a4 A PLANTAGE ? a5 A DISCRETE ? a6 A VOLUME ? a7 A MIX ? e1 DPERFORMANCE ? b0 ? b1 A WCM ? b2 A DOWNSIZE ? b3 A SIZE ? b4 A PLANTAGE ? b5 A DISCRETE ? b6 A VOLUME ? b7 A MIX ? e2 ? 2? ?1? DPERFORMANCE ? d0 ? 1 A WCM ? d2 A ABC ? d3 A DOWNSIZE ? d4 A SIZE ? d5 A PLANTAGE ? d6 A DISCRETE ? d7 A VOLUME ? d8 A MIX ? e3 ?3? In order to test our proposed model, we follow the approach prescribed by Baron and Kenny (1986). Eq. (1) estimates the direct impact of ABC on plant performance. Eq. (2) estimates the fringy impact of the mediating variable, WCM, on plant performance. Eqs. (1) and (2) represent non-nested model speci? cations which estimate the independent impact of ABC and WCM, respectively, on plant performance. Finally, both predictor variables, ABC and WCM, are included in a single regression model speci? d in Eq. (3). We observe that Eq. (2) represents a complete mediation model, whereas Eq. (3) represents a partial mediation model where the impact of ABC is partially mediated through WCM. The dependent variable, DPERFORMANCE, represents the respecti ve change (D) in the three performance measures COST, QUALITY, and TIME. The system of equations estimated separately for each performance measure. We report OLS regression results in put off 4. 8 The estimated coe? cients in the three columns of each panel in Table 4 correspond to the regression models speci? ed in Eqs. (1)(3).First, we estimate the direct impact of ABC on plant performance in the absence of the WCM variable. Estimated regression coe? cients for Eq. (1) are shown in columns (1), (4) and (7) of Table 4 (i. e. , ? rst column of each panel). The regression coe? cient of ABC is statistically signi? cant for DCOST and DTIME (p 0. 10), and it appears that ABC has a positive impact on improvements in plant costs and time to market. 9 ABC does not have signi? cant explanatory power in the DQUALITY regression model as indicated by low R2 values. 8 We also used ordered logit regressions to estimate the system of equations in (1).The ordered logit results are consistent wit h our OLS estimation results. 9 The correct R2 for these models was low (between 1. 38% and 2. 75%) and our analysis of the F-statistics indicates that only the DCOST regression model was signi? cant at p 0. 05. We have not included these results in our tables due to space limitations. 12 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 Table 3 Factors in? uencing WCM implementation ordered logit regression Variable ABC DOWNSIZE SIZE PLANTAGE DISCRETE VOLUME MIX Pseudo-R2 (%) Chi-square N ***, **, * IndicatesLogit coe? cient 0. 50 0. 05 0. 34 A0. 08 A0. 02 0. 212 0. 19 0. 07 82. 67*** (p-value 0. 001) 1250 Chi-square 15. 15*** 0. 56 48. 56*** 1. 73 0. 02 4. 04** 2. 56 signi? cance at the 1%, 5%, and 10% (one-sided) level, respectively. Variable de? nition ABC = 1 if implemented extensively, zero if there is no ABC implementation in the plant. WCM = Six-item summative index that measures the degree of implementation of six types of manufacturing practices JIT, TQM, Kanban, continuous process improvement, competitive benchmarking, self-direct teams. WCM can take any value between zero and six.For each manufacturing practice, 0 = no or some implementation, 1 = extensive implementation D(QUALITY) motley in ? rst-pass quality yield of ? nished products over the last ? ve years 1 = Declined more than 20%, 2 = declined 120%, 3 = no change, 4 = improved 120%, 5 = improved more than 20%. D(COST) Change in unit manufacturing costs, excluding purchased materials, over the last ? ve years 1 = Increased more than 20%, 2 = increased 1120%, 3 = increased 110%, 4 = no change, 5 = decreased 110%, 6 = decreased 1120%, 7 = decreased more than 20%.D(TIME) Factor comprised of the 5-year change in manufacturing cycle time and plant lead time D(Cycle time) Change in manufacturing cycle time over the last ? ve years 1 = No reduction, 2 = decreased 110%, 3 = decreased 1120%, 4 = decreased 2150%, 5 = decreased more than 50%. D(Lead time) Change in customer lead time over the last ? ve years 1 = Increased more than 20%, 2 = increased 120%, 3 = no change, 4 = decreased, 120%, 5 = decreased more than 20%. DISCRETE = 1 if nature of manufacturing operations for primary products is discrete else zero. DOWNSIZE Extent of plant-level downsizing in the past ? e years. 1 = No change, 2 = extent of downsizing increased 110%, 3 = extent of downsizing increased 1120%, 4 = extent of downsizing increased 2150%, 5 = increased 5175%, and 6 = increased more than 75%. SIZE Number of employees at the plant location. 1 = Less than 100 2 = 100249 3 = 250499 4 = 500999 5 = greater than 1000 employees. PLANTAGE Number of years since plant start-up. 1 = Less than 5 years 2 = 510 years 3 = 1120 years 4 = more than 20 years. VOLUME = 1 if plant exhibits high volume production, and zero otherwise. MIX = 1 if plant exhibits high product mix, and zero otherwise.Next, estimated regression coe? cients for Eq. (2) are shown in columns (2), (5) and (8) of Table 4. The regression results indicate that the impact of WCM on all plant performance measures is positive and signi? cant at p 0. 01. In other words, implementation of advanced manufacturing capabilities is associated with improvements in plant costs (b1 = 0. 20, p 0. 01), quality (b1 = 0. 14, p 0. 01), and time to market (b1 = 0. 16, p 0. 01). Hence, our results support hypothesis H2 with respect to the association between WCM implementation and performance. Finally, we estimate the full model in Eq. 3) that includes the direct impact of WCM on plant performance and an additional direct course of study from ABC to the dependent variable. The full model results, as reported in columns (3), (6), and (9) of Table 4, indicate that ABC does not have a direct, signi? cant impact on any of the three measures of plant performance. When the impact of the WCM R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 2. 61 (17. 78)*** 0. 16 (11. 02)*** 0. 05 (0. 83) A0. 04 (A1. 3 3) 0. 01 (0. 51) A0. 02 (A0. 65) 0. 14 (2. 83)*** A0. 02 (A0. 42) 0. 09 (1. 72)* 1250 0. 102 18. 52*** 13 t-Statistics are shown in parentheses. **, **, * Indicates signi? cance at the 1%, 5%, and 10% level, respectively. Note Plant performance is represented using three separate dependent variables. We estimated the three regression models as separate multivariate regressions. variable is included in the model, ABC adoption is not associated with any improvement in plant costs (d2 = 0. 14, t-stat = 1. 43), quality (d2 = A0. 03, t-stat = A0. 47), or time to market (d2 = 0. 05, t-stat = 0. 83). In contrast, WCM continues to have a signi? cant positive impact on all plant performance measures, and the magnitude of the WCM coe? cient is very similar to its estimate in Eq. (2).The adjusted R2 values for the complete mediation models are not signi? cantly di? erent from the R2 values of their corresponding full (i. e. , partial mediation) models. For instance, adding the ABC variable in column (3) results in an increase of 0. 1% (=0. 001) in the DCOST models explanatory power, compared to its corresponding R2 shown in column (2). Similarly, introducing ABC in the DQUALITY and DTIME models, results in statistically insigni? cant increases in model R2 of 0. 0% and 0. 1%, respectively. Hence, our results support hypothesis H3, indicating that WCM completely mediates the impact of ABC on plant performance.We also test an alternative speci? cation based on a perspective that the interaction between ABC and WCM implementation may have an impact on plant performance. The interaction model (Luft & Shields, 2003) is speci? ed as DPERFORMANCE ? c0 ? c1 A WCM ? c2 A ABC ? c3 A ABC A WCM ? c4 A DOWNSIZE ? c5 A SIZE ? c6 A PLANTAGE ? c7 A DISCRETE ? c8 A VOLUME ? c9 A MIX ? e4 (9) Panel C DTIME (8) (7) (6) Panel B DQUALITY (5) (4) (3) 4. 46 (17. 58)*** 0. 20 (7. 79)*** 0. 13 (2. 47)** A0. 11 (A2. 89)*** A0. 23 (A4. 36)*** 0. 05 (0. 61) 0. 22 (2. 52)** 0. 02 (0. 21) 1250 0. 068 14. 19*** 4. 46 (17. 56)*** 0. 9 (7. 62)*** 0. 14 (1. 43) 0. 13 (2. 46)** A0. 11 (A2. 93)*** A0. 23 (A4. 38)*** 0. 05 (0. 65) 0. 22 (2. 52)** 0. 02 (0. 20) 1250 0. 069 12. 68*** 3. 28 (21. 36)*** 0. 024 (0. 37) 0. 016 (0. 48) 0. 009 (0. 40) A0. 062 (A1. 89)* 0. 017 (0. 33) 0. 03 (0. 59) A0. 015 (A0. 24) 1250 0. 002 0. 70 2. 85 (18. 19)*** 0. 14 (8. 78)*** 0. 016 (0. 48) A0. 03 (A1. 28) A0. 06 (A1. 89)* 0. 03 (0. 54) 0. 01 (0. 17) A0. 04 (A0. 64) 1250 0. 056 11. 74*** 2. 86 (18. 19)*** 0. 14 (8. 78)*** A0. 03 (A0. 47) 0. 01 (0. 23) A0. 03 (A1. 27) A0. 05 (A1. 64)* 0. 03 (0. 53) 0. 01 (0. 17) A0. 04 (A0. 64) 1250 0. 056 10. 29*** . 11 (21. 30)*** 0. 11 (1. 82)* A0. 03 (A0. 96) 0. 06 (2. 53)** A0. 03 (A0. 98) 0. 12 (2. 47)** 0. 006 (0. 12) 0. 12 (2. 11)** 1250 0. 014 3. 49** 2. 61 (17. 80)*** 0. 16 (11. 15)*** A0. 04 (A1. 32) 0. 01 (0. 53) A0. 02 (A0. 64) 0. 14 (2. 80)*** A0. 02 (A0. 42) 0. 09 (1. 72)* 1250 0. 101 21. 07*** ?4? The results indicate that the interaction term (i. e. , ABC * WCM) is not statistically signi? cant for any of the plant performance measures. The estimated magnitude of the coe? cient of the interaction term (i. e. , c3) was A0. 04 (p-value = 0. 48), A0. 02 (p-value = 0. 57), and A0. 03 (p-value = 0. 9) for the DCOST, DQUALITY, and DTIME models respectively. These results indicate that the interaction model is not supported by empirical evidence based on analyses of the impact of ABC on operational measures of plant performance. On the other hand, the complete mediation model provides a Table 4 Impact of WCM and ABC on plant performance (2) Panel A DCOST (1) Intercept WCM ABC DOWNSIZE SIZE PLANTAGE DISCRETE VOLUME MIX N Adjusted R2 F Value 5. 05 (20. 50)*** 0. 22 (2. 13)** 0. 142 (2. 63)** 0. 06 (A1. 48) A0. 24 (A4. 54)*** 0. 04 (0. 48) 0. 25 (2. 84)*** 0. 05 (0. 53) 1250 0. 027 5. 93*** 14 R. D. Banker et al. Accounting, Organizations and Society 33 (2008) 119 Table 5 Results of likelihood ratio tests for non-nested model pickax ( N = 1250) Vuongs z-statistic DCOST ABC vs. WCM DQUALITY ABC vs. WCM DTIME ABC vs. WCM 4. 72*** 6. 91*** 7. 45*** p-Value 0. 00 0. 00 0. 00 better explanation of variations in plant performance. Comparison of two non-nested models We compared the R2 values associated with the ABC and WCM models in Table 4, and observe that WCM provides greater explanatory power of the variance in plant performance measures. In order to abstract between these two competing speci? cations (i. e. , ABC Performance versus WCM Performance), we evaluate them as non-nested models using Vuongs (1989) likelihood ratio test for model selection that does not assume under the null that either model is true (Dechow, 1994). It allows us to determine which independent variable (ABC or WCM) has relatively more explanatory power, and represents a more powerful alternative since it can reject one hypothesis in favor of an alternative. We report the results of Vuongs test on nonnested models in Table 5. We conduct t he Vuongs test for each match of competing non-nested model speci? cations in Panels A, B, and C, of Table 4.Comparing the models in Eqs. (1) and (2) for the performance variable DCOST, we ? nd that Vuongs z-statistic of 4. 72 is signi? cant at p 0. 01, which indicates that the WCM model in Eq. (2) provides greater explanatory power of the variance in DCOST, compared to the ABC model in Eq. (1). Similarly, Vuongs z-statistic scores of 6. 91 and 7. 45 are statistically signi? cant (at p 0. 01) for the DQUALITY and DTIME models, respectively. Our results thus indicate that the direct role of ABC in explaining variations in plant performance is relatively small when compared to that of WCM. 10 Contrary to the ? dings reported A signi? cant z-statistic indicates that ABC is rejected in favor of WCM as a better predictor of variance in plant performance. *** Indicates signi? cance at the 1% level. Table 6 Overall impact of ABC on plant performance (N = 1250) Mediated path ABC WCM DC OST ABC WCM DQUALITY ABC WCM DTIME Estimated path coe? cient 0. 08 (0. 02)** 0. 05 (0. 02)** 0. 06 (0. 01)*** p-Values are shown in parentheses. ***, **, * Indicates signi? cance at the 1%, 5%, and 10% level, respectively. in Ittner et al. (2002), our ? ndings imply that the complete mediation model provides a superior speci? ation to study the impact of ABC on plant performance. Estimating the overall impact of ABC We next estimate the magnitude of the overall impact of ABC, based on the pathway that links ABC to DPERF through WCM, where DPERF represents the change (D) in COST, QUALITY, and TIME, respectively. We calculate the magnitude of the overall impact of ABC on DPERF as the cross-product of (a) the marginal impact of ABC on WCM, and (b) the marginal impact of WCM on DPERF. That is o? DPERF? o? DPERF? o? WCM? ? A o? ABC? o? WCM? o? ABC? ?5? 10 We also estimated the model, shown in Fig. 1, using structural equation model (SEM) analyses.We then estimated a reverse causal mo del (i. e. , WCM ABC Performance) to examine whether ABC is a better predictor of performance, compared to WCM. Our SEM ? t statistics for the reverse model fall extracurricular the acceptable range for good model ? t. Consistent with the results reported above, and unlike to the ? ndings reported in Ittner et al. (2002), this suggests that WCM has greater explanatory power than ABC to explain variations in plant performance. The path estimates for the plant performance measures are shown in Table 6. Our results indicate that the overall impact of ABC on DCOST is equal to 0. 8 which is statistically signi? cant at p 0. 05. Similarly, the overall impact of ABC R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 15 on DQUALITY and DTIME are signi? cant, and equal to 0. 05 and 0. 06, respectively. Hence, our results support H3 and indicate that there exists an indirect relationship between ABC and plant performance, where WCM completely mediates the impact of ABC on performance. These results are consistent with our conjectural framework which suggests that, although ABC does not have a direct impact, it has a signi? cant overall impact on performance. 11Discussion We highlight the role played by WCM as a mediator of the impact of ABC on plant performance. We ? nd that ABC has a signi? cant overall impact on reduction in product time to market and unit manufacturing costs, and on improvement in quality. Our results are consistent with prior research which suggests that successful implementation of advanced manufacturing initiatives requires prior adoption of compatible management accounting systems (Milgrom & Roberts, 1995 Shields, 1995 Ittner & Larcker, 1995 Sim & Killough, 1998). Furthermore, our results indicate that WCM practices enable plants to leverage the capabilities o? red by ABC implementation and to signi? cantly improve plant performance. Our study has several limitations. First, the survey instrument measures beliefs abou t changes in plant performance over a ? ve-year period. These measures need to be validated through archival and ? eld data collection in future research. Second, it is possible that ABC may have been in place beforehand or implemented sometime during the ? ve-year period. The secondary nature of the data did not allow us to separate the implications We also extended our research model to study the indirect impact of ABC on change in plant-level matter on assets (ROA), a key ? ancial performance measure. We found that ABC has a signi? cant, positive impact on DROA which is mediated through its impact on WCM. Our ROA results are consistent with our results on the inter-relationships between ABC, WCM, and plant operational performance reported here. 11 of these possibilities. Future studies must be designed to gather more detailed data, about the timeline of ABC implementation to better understand its impact on plant performance especially since users may need training to adapt to ne w types of costing procedures.ABC implementation was measured as a 01 variable in our study. It is possible that using a more granular scale to measure the extent of ABC implementation, including the level of ABC integration and the time lag since ABC implementation, may provide greater insights on the relationship between ABC and plant performance. Our focus on plants that employ a minimum of 100 employees limits the generalizability of our results to industries with relatively large or very small manufacturing plants. We also did not account for country or cultural di? rences in manufacturing characteristics since the scope of the survey was trammel to US plants. Our ? ndings must also be validated with additional data collected in industry-speci? c settings to examine the impact of industry characteristics and di? erences in manufacturing strategies. Future research may also include evaluation of other contextual factors that are associated with the success of ABC implementation , such as process infrastructure, and the extent of human resource support and outsourcing. Our study enhances the quality of the surviving body of knowledge on ABC e? ectiveness in several ways.First, our survey responses were data provided by plant managers who may represent a more objective and knowledgeable source of plant-wide operations compared to many previous studies, that relied on respondents (such as ABC project managers) with a personal interest group in ABC success (Shields, 1995 Swenson, 1995). Second, ABC non-adopters were identi? ed based on the responses provided by plant managers, unlike prior studies where non-adopters were identi? ed based on the lack of public information on ABC implementation (Balakrishnan, Linsmeier, & Venkatachalam, 1996 Gordon & Silvester, 1999).Third, we interact the manufacturing plant (instead of the ? rm) as the unit of analysis, which allowed us to observe the impact of ABC implementation on changes in process-level performance metr ics 16 R. D. Banker et al. / Accounting, Organizations and Society 33 (2008) 119 and avoid the confounding potential when only ? rm-level ? nancial measures are used. Acknowledgement Helpful suggestions by the Editor and two anon. referees are gratefully acknowledged. Conclusion In contrast to prior studies (Ittner et al. 2002) that have typically focused on the direct impact of ABC on plant performance, we study the role of world-class manufacturing practices in mediating the impact of ABC on plant performance. We draw on prior research on the relationship between management accounting systems and business processes to better understand how ABC may support implementation of WCM practices. Analyzing data from a large cross-sectional sample of US manufacturing plants, we ? nd evidence supporting our model emphasizing the role of advanced manufacturing practices in improving plant performance. Our ? ndings evince the need for ? ms to strengthen their manufacturing capabilities when m aking an investment to implement ABC systems, as ABC is unlikely to result in improved manufacturing performance by itself. Our evidence also suggests that plants can reap signi? cant bene? ts by combining ABC implementation with the deployment of advanced manufacturing practices. Using a conceptual lens that focuses on the indirect impact of ABC, the evidence supports our alternative theoretical perspective to prior research. We conceptualize ABC as only an enabler of world-class manufacturing practices, which in turn is associated with improvements in plant performance.Our complete mediation model stands in contrast with earlier models proposed by Ittner et al. (2002) who focus primarily on the direct impact of ABC on plant performance. The results indicate that our alternative conceptualization is superior in terms of its ability to explain variations in plant performance based on cross-sectional data of a large sample of plants that have implemented ABC. Furthermore, our propose d model may provide an avenue for future researchers using di? erent methodologies to explain di? erences in performance improvements following ABC implementations.It may also explain the weak or doubtful results in prior research on ABC impact because ABC adoption may not be a su? cient statistic for WCM. Appendix check into questions I. Plant characteristics Variable SIZE Question How many employees are at this plant location? 1 = Less than 100 2 = 100249 3 = 250499 4 = 500999 5 = 1000 employees PLANTAGE How many years has it been since plant start-up? 1 = Less than 5 years 2 = 510 years 3 = 1120 years 4 = 20 years MIX, VOLUME12 How would you describe the primary product mix at this plant? = laid-back volume, high mix 2 = High volume, low mix 3 = Low volume, high mix 4 = Low volume, low mix What is the nature of manufacturing operations for primary products at this plant? 1 = decided 0 = Otherwise (hybrid or process) What is the extent of downsizing at the plant in the past ? ve years? 1 = no change, 2 = extent of downsizing increased 110%, 3 = increased 1120%, 4 = increased 2150%, 5 = increased 5175%, and 6 = increased 75% DISCRETE DOWNSIZE For our analysis, we split the data into two variables such that MIX = 1 if high mix 0 = otherwise, and VOLUME = 1 if high volume 0 = otherwise